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» Subsystem Based Generalizations of Rough Set Approximations
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FUZZIEEE
2007
IEEE
15 years 3 months ago
Distance Measure Assisted Rough Set Feature Selection
Abstract— Feature Selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most use...
Neil MacParthalain, Qiang Shen, Richard Jensen
GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
14 years 10 months ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
RSKT
2010
Springer
14 years 7 months ago
Naive Bayesian Rough Sets
A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a...
Yiyu Yao, Bing Zhou
ICNC
2009
Springer
15 years 2 months ago
Knowledge Acquisition Approach Based on Rough Set and Artificial Neural Network in Product Design Process
In this paper, product structure is taken as knowledge acquisition point, and the effective knowledge acquisition path is discussed by establishing the associated relationship bet...
Changfeng Yuan, Wanlei Wang, Yan Chen
EWCBR
2006
Springer
15 years 1 months ago
Rough Set Feature Selection Algorithms for Textual Case-Based Classification
Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain)...
Kalyan Moy Gupta, David W. Aha, Philip Moore